Food & beverage case study: ₹25,000 to ₹31,734 monthly revenue in 4 months

New ads per month

1. 1

   Research & offer · Month 1

   What we do

   We start with a clear strategy, which the brand in this scenario named first, before anything else on this smaller store. We open with three documents: a website audit, a creative brief and a media schedule by month. We set basket-size offers that reward a second and third item in the cart.

   Why

   In this case study, a smaller food and beverage brand grows monthly revenue in 4 months, from ₹25,000 to ₹20L (80x). Nine in ten of the accounts we measured grew more slowly than that in the same time; the case study is built on what the fastest tenth reached, and shows the gap to the agreed number honestly. The main problem in this scenario is a clear strategy. A written brief gives every later budget decision a reference point. A larger basket spreads the cost of each order over more revenue.

   How it works

   The audit, the brief and the media schedule are shared before spend rises. Offers are tuned to the order values that already sell.

2. 2

   Measurement & reviews · Month 1 to 4

   What we do

   We track ad-platform revenue beside store orders in a shared daily sheet. We agree a written number for every month on the way from ₹25,000 to ₹20L, and start each review with the month-to-date figure against it. We set a written rule: no budget step in a month where return falls too far to pay for it.

   Why

   It did not report a return on ad spend, so the case study starts from what measured food and beverage stores of that size hold. Platform attribution over-counts, so budget decisions sit on the store-side number. A missed month shows up early instead of at the end of the case study.

   How it works

   The sheet is read before each budget change. Written monthly numbers make each scaling decision explicit. A month whose return slips past that point keeps its budget instead.

3. 3

   Creative testing · Month 1

   What we do

   We lead the testing layer with video, catalogue ads and static image, building to about a dozen new ads a month by the final month, with every format close to the account's return. We run one broad video campaign of the core products, with a customer testimonial, and give it most of the budget. We commission ads in batches, and read each batch for a set window before ordering the next. The learning month runs at a low daily budget and moves up only when orders come through.

   Why

   An everyday product sells best when the algorithm is free to find its buyers. Buying more before a batch is read means paying for guesses. Early spend buys learning, not scale.

   How it works

   A video that takes off gets the budget; tests that do not convert are cut. Losing batches stop; winning ads take their budget. Only confirmed orders at the low budget unlock the next step. More spend buys more new ads, led by video ahead of catalogue ads.

4. 4

   Scaling · Month 2 to 3

   What we do

   Through Month 2 to Month 3, we push toward ₹20L: the budget rises gently while return holds, and Instagram Reels carries the most spend and Instagram Feed the next. We let return decide budget: more while it holds, less when it slips. We front-load about a third of each month's budget into the first week.

   Why

   Across Month 2 to Month 3, the budget rises gently, while return on spend holds. Two of these months stop their budget step where return slips too far. Return holds through these months because each budget step stops where return starts to slip. Early-month demand is stronger for repeat consumables.

   How it works

   There is no fixed ramp; each month's budget follows the return of the last. The monthly budget curve is weighted to the opening days. In the final month, Instagram Reels takes the most spend and Instagram Feed the next most. Cold audiences take most of the budget; warm audiences return more per rupee.

5. 5

   Steady state · Month 4

   What we do

   From Month 4, we hold the gains and push toward ₹20L only as far as return allows. We hold a creative bank and swap tired ads out before click-through falls. We recover abandoned checkouts and lift repeat orders with WhatsApp messages.

   Why

   From Month 4 growth slows while revenue is still under ₹20L. Budget keeps rising, more slowly than in the scaling months. In one account we measured, click-through fell before revenue did; refresh is how that is avoided. WhatsApp is cheaper than paid retargeting for buyers who already reached checkout.

   How it works

   New creatives mix with proven ones rather than replacing them all at once. WhatsApp runs alongside paid retargeting, not instead of it.

1. Month 1

   The learning phase opens: tracking is checked against store orders and the first ads go live on a small daily budget. The media schedule and creative brief are signed off. Store orders and platform revenue are reconciled in the shared sheet.

   • Revenue ₹24,476
   • ROAS 1.93x
   • Ad spend ₹12,662
2. Month 2

   Scaling begins: the creative bank supplies this period's refresh. Only the part of the step that return supports is taken: spend steps up, and return holds.

   • Revenue ₹27,175
   • ROAS 1.92x
   • Ad spend ₹14,165
3. Month 3

   This batch's read is done: winners stay, the rest are cut.

   • Revenue ₹29,873
   • ROAS 1.87x
   • Ad spend ₹16,009
4. Month 4

   Steady phase begins: creative refresh and repeat orders take on more of the work. Only the part of the step that return supports is taken: spend holds, and return holds. The case study finishes short of ₹20L: budget stops rising where return starts to slip. Each order costs about what it did while learning.

   • Revenue ₹31,734
   • ROAS 1.87x
   • Ad spend ₹16,996

1. 1

   Moved a third of spend into one broad video campaign at 3.6x

2. 2

   Monthly review calls that start with what has not worked

3. 3

   Run by region for regional festivals, and pan-India for Dasara and Diwali